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Bayesian decision making in human collectives with binary choices.

Víctor M Eguíluz1, Naoki Masuda2, Juan Fernández-Gracia3

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Summary
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Humans making binary choices, like answering questions, can be predicted using a Bayesian model. Peer influence on decisions is independent of question difficulty, challenging existing theories.

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Area of Science:

  • Social Psychology
  • Behavioral Economics
  • Computational Social Science

Background:

  • Understanding information aggregation and decision-making is crucial for explaining societal phenomena like trends and the wisdom of crowds.
  • Agents often face choices between discrete options, necessitating models for binary decision-making.

Purpose of the Study:

  • To analyze human binary opinion choices using experimental data.
  • To evaluate the effectiveness of a Bayesian approach in modeling information aggregation and decision-making.
  • To investigate the influence of peer information on individual choices.

Main Methods:

  • Conducted two experiments involving human participants answering binary-response questions.
  • Collected data on choices made with and without information about previous participants' answers.
  • Applied a Bayesian model to analyze the probability of answer choices and compared it with existing functions.

Main Results:

  • A Bayesian approach effectively captures the probability of choosing between binary options.
  • Peer influence on choices was found to be uncorrelated with question difficulty.
  • Experimental data contradicted Weber's law regarding the influence of previous choices.

Conclusions:

  • The proposed Bayesian model offers a simple and mechanistic explanation for observed human decision-making behavior.
  • While performing reasonably well, the Bayesian model's fit was comparable to, though sometimes slightly outperformed by, other functions.
  • The findings provide insights into how individuals aggregate information and make decisions in social contexts.